elastic-job分布式调度与zookeeper的简单应用
作者：互联网
一、对分布式调度的理解
调度—>定时任务，分布式调度—>在分布式集群环境下定时任务这件事
Elastic-job（当当⽹开源的分布式调度框架）
1 定时任务的场景
定时任务形式：每隔⼀定时间/特定某⼀时刻执⾏ 例如：
订单审核、出库 订单超时⾃动取消、⽀付退款 礼券同步、⽣成、发放作业 物流信息推送、抓取作业、退换货处理作业
数据积压监控、⽇志监控、服务可⽤性探测作业 定时备份数据
⾦融系统每天的定时结算 数据归档、清理作业 报表、离线数据分析作业
2 什么是分布式调度
什么是分布式任务调度？有两层含义
1）运⾏在分布式集群环境下的调度任务（同⼀个定时任务程序部署多份，只应该有⼀个定时任务在执
⾏）
2）分布式调度—>定时任务的分布式—>定时任务的拆分（即为把⼀个⼤的作业任务拆分为多个⼩的作 业任务，同时执⾏）
3、分布式调度Elastic-Job与zookeeperk
特点优点
-
轻量级去中⼼化
2、任务分⽚
1、ElasticJob可以把作业分为多个的task（每⼀个task就是⼀个任务分⽚），每⼀个task交给具体的⼀个机器2、实例去处理（⼀个机器实例是可以处理多个task的），但是具体每个task 执⾏什么逻辑由我们⾃⼰来指定。
3、默认是平均去分，可以定制。分⽚项也是⼀个JOB配置，修改配置，重新分⽚，在下⼀次定时运⾏之前会重新调⽤分⽚算法
结果就是：哪台机器运⾏哪⼀个⼀⽚，这个结果存储到zookeeperk中的，主节点会把分⽚给分好 放到注册中⼼去，然后执⾏节点从注册中⼼获取信息(执⾏节点在定时任务开启的时候获取相应的分⽚
2）如果所有的节点挂掉值剩下⼀个节点，所有分⽚都会指向剩下的⼀个节点，这也是ElasticJob的⾼可⽤。
3、 弹性扩容
总结：
分布式调度ElasticJob目的是解决某一个job节点的服务器压力（一个人做，和一堆人分工去做的）利用zookeeperk 轻量级去中⼼、任务分⽚、弹性扩容 三大特点，实现分片分工。快速有效、协调完成工作。不会出现分片重复工作的情况。
二、准备验证环境
1、安装zookeeper
https://www.cnblogs.com/aGboke/p/12904932.html
zooInspector的使用： https://www.cnblogs.com/lwcode6/p/11586537.html
elastic-job：https://github.com/elasticjob
2、搭建maven项目、引入
<!--数据库驱动jar--> <dependency> <groupId>mysql</groupId> <artifactId>mysql-connector-java</artifactId> <version>5.1.46</version> </dependency> <!--任务调度框架quartz--> <!--org.quartz-scheduler/quartz --> <dependency> <groupId>org.quartz-scheduler</groupId> <artifactId>quartz</artifactId> <version>2.3.2</version> </dependency> <!--elastic-job-lite-core--> <dependency> <groupId>com.dangdang</groupId> <artifactId>elastic-job-lite-core</artifactId> <version>2.1.5</version> </dependency>3、测试代码
package com.lagou.job; import com.dangdang.ddframe.job.api.ShardingContext; import com.dangdang.ddframe.job.api.simple.SimpleJob; import com.dangdang.ddframe.job.config.JobCoreConfiguration; import com.dangdang.ddframe.job.config.simple.SimpleJobConfiguration; import com.dangdang.ddframe.job.lite.api.JobScheduler; import com.dangdang.ddframe.job.lite.config.LiteJobConfiguration; import com.dangdang.ddframe.job.reg.base.CoordinatorRegistryCenter; import com.dangdang.ddframe.job.reg.zookeeper.ZookeeperConfiguration; import com.dangdang.ddframe.job.reg.zookeeper.ZookeeperRegistryCenter; import java.util.List; import java.util.Map; /** * @author Mrwg * @date 2020/5/15 * @description */ public class BackupJob implements SimpleJob { @Override public void execute(ShardingContext shardingContext){ /* 从resume数据表查找1条未归档的数据，将其归档到resume_bak 表，并更新状态为已归档（不删除原数据） */ // 查询出⼀条数据 String selectSql = "select * from resume where state='未归档' limit 1"; List<Map<String, Object>> list = JdbcUtil.executeQuery(selectSql); if (list == null || list.size() == 0) { return; } Map<String, Object> stringObjectMap = list.get(0); long id = (long) stringObjectMap.get("id"); String name = (String) stringObjectMap.get("name"); String education = (String) stringObjectMap.get("education"); // 打印出这条记录 System.out.println("======>>>id：" + id + " name：" + name + " education：" + education); // 更改状态 String updateSql = "update resume set state='已归档' where id=?"; JdbcUtil.executeUpdate(updateSql, id); // 归档这条记录 String insertSql = "insert into resume_bak select * from resume where id=?"; JdbcUtil.executeUpdate(insertSql, id); } public static void main(String[] args) { //配置分布式Zookeeper分布式协调中心 ZookeeperConfiguration zookeeperConfiguration = new ZookeeperConfiguration("ip:2181", "elastic-job"); CoordinatorRegistryCenter coordinatorRegistryCenter = new ZookeeperRegistryCenter(zookeeperConfiguration); coordinatorRegistryCenter.init(); //配置任务 每秒运行一次 JobCoreConfiguration jobCoreConfiguration = JobCoreConfiguration.newBuilder("archive-job", "1 * * * * ?", 1).build(); SimpleJobConfiguration simpleJobConfiguration = new SimpleJobConfiguration(jobCoreConfiguration, BackupJob.class.getName()); //启动任务 new JobScheduler(coordinatorRegistryCenter, LiteJobConfiguration.newBuilder(simpleJobConfiguration).build()).init(); } }package com.lagou.job; import java.sql.*; import java.util.ArrayList; import java.util.HashMap; import java.util.List; import java.util.Map; /** * @author Mrwg * @date 2020/5/15 * @description */ public class JdbcUtil { //url private static String url = "jdbc:mysql://localhost:3306/test?characterEncoding=utf8&useSSL=false"; //user private static String user = ""; //password private static String password = ""; //驱动程序类 private static String driver = "com.mysql.jdbc.Driver"; static { try { Class.forName(driver); } catch (ClassNotFoundException e) { // TODO Auto-generated catch block e.printStackTrace(); } } public static Connection getConnection() { try { return DriverManager.getConnection(url, user, password); } catch (SQLException e) { // TODO Auto-generated catch block e.printStackTrace(); } return null; } public static void close(ResultSet rs, PreparedStatement ps, Connection con) { if (rs != null) { try { rs.close(); } catch (SQLException e) { // TODO Auto-generated catch block e.printStackTrace(); } finally { if (ps != null) { try { ps.close(); } catch (SQLException e) { // TODO Auto-generated catch block e.printStackTrace(); } finally { if (con != null) { try { con.close(); } catch (SQLException e) { // TODO Auto-generated catch block e.printStackTrace(); } } } } } } } public static void executeUpdate(String sql, Object... obj) { Connection con = getConnection(); PreparedStatement ps = null; try { ps = con.prepareStatement(sql); for (int i = 0; i < obj.length; i++) { ps.setObject(i + 1, obj[i]); } ps.executeUpdate(); } catch (SQLException e) { // TODO Auto-generated catch block e.printStackTrace(); } finally { close(null, ps, con); } } public static List<Map<String, Object>> executeQuery(String sql, Object... obj) { Connection con = getConnection(); ResultSet rs = null; PreparedStatement ps = null; try { ps = con.prepareStatement(sql); for (int i = 0; i < obj.length; i++) { ps.setObject(i + 1, obj[i]); } rs = ps.executeQuery(); List<Map<String, Object>> list = new ArrayList<>(); int count = rs.getMetaData().getColumnCount(); while (rs.next()) { Map<String, Object> map = new HashMap<String, Object>(); for (int i = 0; i < count; i++) { Object ob = rs.getObject(i + 1); String key = rs.getMetaData().getColumnName(i + 1); map.put(key, ob); } list.add(map); } return list; } catch (SQLException e) { // TODO Auto-generated catch block e.printStackTrace(); } finally { close(rs, ps, con); } return null; } } JdbcUtilCREATE TABLE `resume` ( `id` bigint(20) NOT NULL AUTO_INCREMENT, `name` varchar(255) DEFAULT NULL, `sex` varchar(255) DEFAULT NULL, `phone` varchar(255) DEFAULT NULL, `address` varchar(255) DEFAULT NULL, `education` varchar(255) DEFAULT NULL, `state` varchar(255) DEFAULT NULL, PRIMARY KEY (`id`) USING BTREE ) ENGINE=InnoDB AUTO_INCREMENT=0 DEFAULT CHARSET=utf8; create table resume_bak like resume; INSERT INTO `test`.`resume`(`id`, `name`, `sex`, `phone`, `address`, `education`, `state`) VALUES (1, '2', 'girl', '18801240649', '北京', '本科', '已归档'); INSERT INTO `test`.`resume`(`id`, `name`, `sex`, `phone`, `address`, `education`, `state`) VALUES (2, '2', 'girl2', '18801240649', '北京', '本科', '已归档'); INSERT INTO `test`.`resume`(`id`, `name`, `sex`, `phone`, `address`, `education`, `state`) VALUES (3, '3', 'girl3', '18801240649', '北京', '本科', '已归档'); INSERT INTO `test`.`resume`(`id`, `name`, `sex`, `phone`, `address`, `education`, `state`) VALUES (4, '4', 'girl4', '18801240649', '北京', '本科', '已归档'); INSERT INTO `test`.`resume`(`id`, `name`, `sex`, `phone`, `address`, `education`, `state`) VALUES (5, '5', 'girl5', '18801240649', '北京', '本科', '已归档'); INSERT INTO `test`.`resume`(`id`, `name`, `sex`, `phone`, `address`, `education`, `state`) VALUES (6, '6', 'girl6', '18801240649', '北京', '本科', '已归档'); INSERT INTO `test`.`resume`(`id`, `name`, `sex`, `phone`, `address`, `education`, `state`) VALUES (7, '7', 'girl7', '18801240649', '北京', '本科', '已归档'); INSERT INTO `test`.`resume`(`id`, `name`, `sex`, `phone`, `address`, `education`, `state`) VALUES (8, '8', 'girl8', '18801240649', '北京', '本科', '已归档'); INSERT INTO `test`.`resume`(`id`, `name`, `sex`, `phone`, `address`, `education`, `state`) VALUES (9, '9', 'girl9', '18801240649', '北京', '本科', '已归档'); INSERT INTO `test`.`resume`(`id`, `name`, `sex`, `phone`, `address`, `education`, `state`) VALUES (10, '10', 'girl10', '18801240649', '北京', '本科', '已归档'); INSERT INTO `test`.`resume`(`id`, `name`, `sex`, `phone`, `address`, `education`, `state`) VALUES (11, '11', 'girl11', '18801240649', '北京', '本科', '已归档'); INSERT INTO `test`.`resume`(`id`, `name`, `sex`, `phone`, `address`, `education`, `state`) VALUES (12, '12', 'girl12', '18801240649', '北京', '本科', '已归档'); INSERT INTO `test`.`resume`(`id`, `name`, `sex`, `phone`, `address`, `education`, `state`) VALUES (13, '13', 'girl13', '18801240649', '北京', '本科', '已归档'); INSERT INTO `test`.`resume`(`id`, `name`, `sex`, `phone`, `address`, `education`, `state`) VALUES (14, '14', 'girl14', '18801240649', '北京', '本科', '已归档'); INSERT INTO `test`.`resume`(`id`, `name`, `sex`, `phone`, `address`, `education`, `state`) VALUES (15, '15', 'girl15', '18801240649', '北京', '本科', '已归档'); INSERT INTO `test`.`resume`(`id`, `name`, `sex`, `phone`, `address`, `education`, `state`) VALUES (16, '16', 'girl16', '18801240649', '北京', '本科', '已归档'); INSERT INTO `test`.`resume`(`id`, `name`, `sex`, `phone`, `address`, `education`, `state`) VALUES (17, '17', 'girl17', '18801240649', '北京', '本科', '已归档'); INSERT INTO `test`.`resume`(`id`, `name`, `sex`, `phone`, `address`, `education`, `state`) VALUES (18, '18', 'girl18', '18801240649', '北京', '本科', '已归档'); INSERT INTO `test`.`resume`(`id`, `name`, `sex`, `phone`, `address`, `education`, `state`) VALUES (19, '19', 'girl19', '18801240649', '北京', '本科', '已归档'); INSERT INTO `test`.`resume`(`id`, `name`, `sex`, `phone`, `address`, `education`, `state`) VALUES (20, '20', 'girl20', '18801240649', '北京', '本科', '已归档'); INSERT INTO `test`.`resume`(`id`, `name`, `sex`, `phone`, `address`, `education`, `state`) VALUES (21, '21', 'girl21', '18801240649', '北京', '本科', '已归档'); INSERT INTO `test`.`resume`(`id`, `name`, `sex`, `phone`, `address`, `education`, `state`) VALUES (22, '22', 'girl22', '18801240649', '北京', '本科', '已归档'); INSERT INTO `test`.`resume`(`id`, `name`, `sex`, `phone`, `address`, `education`, `state`) VALUES (23, '23', 'girl23', '18801240649', '北京', '本科', '已归档'); INSERT INTO `test`.`resume`(`id`, `name`, `sex`, `phone`, `address`, `education`, `state`) VALUES (24, '24', 'girl24', '18801240649', '北京', '本科', '已归档'); sql脚本4、启动main()方法 ，zooInspector 链接 zookeeper
1、启动一个实列
当前定时任务，全部在当前实列下执行，启动俩个实列，zk会重新计算分片和竞争机制，来确定那台机器运行当前任务。（一般情况下第二个实列会拿到领导权），当我们把俩个实列，
其中一个停掉，第一个实列会继续接着运行未完成的任务。 如下下边gif所示。运行速度受当前网络、机器硬件影响。
2、启动俩个实列
3、调整分片数量
- 3个分片，启动1个main()方法（如下所示gif）
JobCoreConfiguration jobCoreConfiguration = JobCoreConfiguration.newBuilder("archive-job", "1 * * * * ?", 3).build();// 任务名称 执行时间 分片数
- 3个分片2个main()
- 3个分片3个main()实例
标签：name,elastic,resume,zookeeper,式调度,state,归档,education,id 来源： https://blog.csdn.net/w1033162186/article/details/121316294